Upload
grssieee
View
909
Download
2
Embed Size (px)
Citation preview
July 25, 2011
Mitg
lied
de
r H
elm
ho
ltz-G
em
ein
sch
aft
Recent Advances in Object-based Change Detection
IGARSS 2011, Vancouver
Change Detection and Multitemporal Image Analysis I
| Irmgard Niemeyer, Clemens Listner Nuclear Safeguards Group Institute of Energy and Climate Research IEK-6: Nuclear Waste Management and Reactor Safety Forschungszentrum Jülich GmbH, Germany
Slide 2
Acknowledgments
German Support Programme for the
International Atomic Energy Agency (IAEA)
Project on satellite imagery analysis and photo
interpretation support“
EC FP7, Global Monitoring for Environment and
Security (GMES)
Current project G-MOSAIC
General R&D interests
Methodological developments, PhD thesis Listner
Slide 3
Recent Advances in Object-based Change Detection
Slide 4
Very high spatial resolution optical sensors (<1m): WorldView-2
Slide 5
Object-based change detection using
IR-MAD
Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) [Nielsen 2007]
Linear transformation of the feature space aimed to enhance the change information in the difference image
Modeling object’s feature vector as random vectors F
and G of length N
Transformation of vectors to enhance relevant changes
var(m1
= a1
TU - b1
TV) → max under the constraint that var(a
1TU) = var(b
1TV) = 1
Further orthogonal variates mi can be computed
Σmi2 ~ Chi2 indicating change probability P(change)
Iteration by weighting with 1- P(change)
Additional step: Application of PCA to U and V
1. Introduction
Slide 6
Statistical pixel-based change detection approaches provide good results, but shows limits due to …
• low number of spectral channels or small spectral range covered,
• image registration problems.
Object-based change detection looks promising, but …
• how to connect corresponding objects?
• how to carry out a reasonable segmentation for this task?
Object-based change detection using
IR-MAD
1. Introduction
Slide 7
Existing approaches to segmentation
for object-based change detection
Segment I1 and I
2 as stack
• segmentation not adequate for I1
and I2
• shape features cannot be used
Use segmentation of I1 for I
2
• segmentation not adequate for I2
• shape features cannot be used
Independent segmentation
• leads to false-alarm segment changes
• shape features can be used
Time 1
Time 2
Image data
Segmentation levels
Time 1
Time 2
Image data
Segmentation levels
Time 1
Time 2
Image data
Segmentation levels
2. Segmentation
Slide 8
Multiresolution segmentation
Region-based bottom-up approach to segmentation
Each segment is a binary tree (leafs=pixel, root=final segment)
Implemented in eCognitionTM
Starts with chessboard segmentation
Selects iteratively a segment X and merges it to a
neighboring segment Y if
)),((min),(
)(ZXdYXd
XNZ
)),((min),()(
ZYdXYdYNZ
TXYd ),(
2. Segmentation
Slide 9
Multiresolution segmentation
2. Segmentation
Slide 10
Segmentation of identical images up to Gaussian noise (μ=0,σ=0.1) using multiresolution segmentation
Multiresolution segmentation applied to slightly different images
2. Segmentation
Slide 11
Multiresolution segmentation adapted
for object-based change detection 1
1. Segment I1 using multiresolution segmentation
2. Apply this segmentation to I2 and recalculate color
heterogeneity
3. Check each merge for consistency with I2 using a
predefined test
4. Remove inconsistent segments using a predefined removal strategy
5. Re-run the multiresolution segmentation using the so
gained segmentation of I2 as an initial segmentation
2. Segmentation
Slide 12
Multiresolution segmentation adapted
for object-based change detection 2
Given segment S3 with children S
1 (seed) and S2
Threshold test
• h(S3) ≤ T
check in I
2 ?
Local best fitting test
• Is S2 the locally best fitting neighbor for S
1 in I2 ?
Local mutual best fitting test
• Are S1 and S2 local mutually best fitting in I
2 ?
Reduce sensitivity of the best fitting tests by using
Tchecktolerance
2. Segmentation
Slide 13
Segmentation for object-based change detection Threshold test & universal segment removal strategy
2. Segmentation
Slide 14
Segmentation for object-based change detection Local mutual best fitting test & global segment removal strategy
2. Segmentation
Slide 15
Segmentation for object-based change detection Local best fitting test & local segment removal strategy
2. Segmentation
Slide 16
Segmentation for object-based change detection Threshold test & universal segment removal strategy
2. Segmentation
Slide 17
Object correspondence for object- based change detection
Directed Via intersection
1
1
i x i
n
i y k
k=
x = f S ,
y = f Tn
1
2
i x
i y
x = f S ,
y = f S
3. Object correspondence
Slide 18
Object-based change detection
4. Experiments
Post-processing Integration to GIS or GDBS
Change classification
Class-based FFN Marpu 2009
Change detection
IR-MAD Nielsen 2007, Listner & Niemeyer
2011b
Segmentation Multiresolution segmentation adapted
to change detection
e.g. Listner & Niemeyer 2010, 2011a,
2011b
Pre-processing Image-to-image registration,
Radiometric normalization
Canty & Nielsen 2009
Slide 19
Object-based change detection
4. Experiments
Slide 20
Object-based change detection
Segmentation of the bitemporal imagery using threshold test and universal segment removal strategy.
4. Experiments
Slide 21
Object-based change detection
Directed change detection. Changes from time 1 to time 2 (left) and from time 2 to time 1 (right).
4. Experiments
Slide 22
Object-based change detection
Change detection using intersected objects.
Change detection using MAD
objects.
4. Experiments
Slide 23
Object-based change detection Accuracy assessment
Directed change detection: T1T2
Directed change detection: T2T1
Change detection using intersected objects
Change detection using MAD objects
Overall accuracy
0.98 0.98 0.98 0.99
KIA 0.82 0.87 0.77 0.75
4. Experiments
Slide 24
Summary
An enhanced procedure for segmentation was introduced and implemented into the change detection workflow.
Moreover, numerically issues in the IR-MAD method were addressed.
The proposed methods showed good results in three experiments using aerial imagery.
Further developments are needed:
• New consistency tests and segment removal strategies;
• methods for enabling the user to easily select the segmentation parameters, e.g. by using training samples;
• implementation as eCognition plugin.
5. Summary
Slide 25
Most recent publications
C. Listner and I. Niemeyer (2011a), “Advances in object-based change detection,” Proc. IGARSS 2011, Vancouver, July 2011
C. Listner and I. Niemeyer (2011b), “Object-based change detection,” Photogrammetrie, Fernerkundung, Geoinformation (PFG), vol. 3, 2011 (in print)
Slide 26
Thank you for your attention.
Dr. Irmgard Niemeyer Nuclear Safeguards Institute of Energy and Climate Research IEK-6: Nuclear Waste Management and Reactor Safety Forschungszentrum Jülich GmbH in der Helmholtz-Gemeinschaft | 52425 Jülich | Germany Phone / Fax: +49 2461 61-1762 / -2450 Email: [email protected] www.fz-juelich.de/ief/iek-6/